Orginal Article: Land-Use Changes in Southern Appalachian Landscapes: Spatial Analysis and Forecast Evaluation

Abstract

Understanding human disturbance regimes is crucial for developing effective conservation and ecosystem management plans and for targeting ecological research to areas that define scarce ecosystem services. We evaluate and develop a forecasting model for land-use change in the Southern Appalachians. We extend previous efforts by (a) addressing the spatial diffusion of human populations, approximated by building density, (b) examining a long time period (40 years, which is epochal in economic terms), and (c) explicitly testing the forecasting power of the models. The resulting model, defined by linking a negative binomial regression model of building density with a logit model of land cover, was fit using spatially referenced data from four study sites in the Southern Appalachians. All fitted equations were significant, and coefficient estimates indicated that topographic features as well as location significantly shape population diffusion and land use across these landscapes. This is especially evident in the study sites that have experienced development pressure over the last 40 years. Model estimates also indicate significant spatial autocorrelation in land-use observations. Forecast performance of the models was evaluated by using a separate validation data set for each study area.Depending on the land-use classification scheme, the models correctly predicted between 68% and 89% of observed land uses. Tests based on information theory reject the hypothesis that the models have no explanatory power, and measures of entropy and information gain indicate that the estimated models explain between 47% and 66% of uncertainty regarding land-use classification. Overall, these results indicate that modeling land-cover change alone may not be useful over the long run, because changing land cover reflects the outcomes of more than one human process (for example, agricultural decline and population growth). Here, additional information was gained by addressing the spatial spread of human populations. Furthermore, coarse-scale measures of the human drivers of landscape change (for example, population growth measured at the county level) appear to be poor predictors of changes realized at finer scales. Simulations demonstrate how this type of approach might be used to target scarce resources for conservation and research efforts into ecosystem effects.

Map of changes in landscape naturalness in the Fiastra and Salino catchment basins (central Italy)

“…Describing and analysing proportional change of different land cover types through time can prove highly valuable, not only for preserving biological diversity, but also for developing general landscape models useful for ecosystem management, environmental policies, and human welfare (Christensen et al, 1996;Franklin, 1993;Jobin, Latendresse, Grenier, Maisonneuve, & Sebbane, 2010). It is thought that landscape changes are driven by complex socioeconomic interactions (Forman, 1995;Irwin & Geoghegan, 2001;Wear & Bolstad, 1998;Zonneveld, 1995), but environmental factors (first of all, physical and bioclimatic ones) often influence land use as well (Blasi, Smiraglia, & Carranza, 2003). Analysis of environmental features that affect landscape ecology and dynamics can be performed by interpreting the factors that affect a territory in terms of different hierarchically determined spatio-temporal intervals (Allen & Starr, 1982;Catorci, Orsomando, & Silvi, 1995;King, 1977; ISSN 1744-5647 online # 2012 Andrea Catorci, Monica Foglia, Federico Maria Tardella and Alessandra Vitanzi http://dx.doi.org/10.1080/17445647.2012.668769 http://www.tandfonline.com O'Neill & King, 1998).…”

Evaluating sampling strategies and logistic regression methods for modelling complex land cover changes

“…2004a), and because we were interested ultimately in comparing the systematic changes in land cover to each other, and not necessarily to the static state. For each sampling strategy, we sampled an independent data set without replacement from the original data pool by the same methods (data-splitting) for subsequent model evaluation (Wear & Bolstad 1998).…”

Approaching the concepts of ecosystems resilience and stability through spatiotemporal system dynamics and agent-based modelling

“…Similarly, there have been field approximations around the world, mostly in temperate areas [17,21,22]. There is also a need for specific research on the characteristics of ecosystems, the degree of human intervention [23,24], the degree of invasion, degradation [13], and resilience and stability [2,25].…”